Face recognition using Krawtchouk moment

Feature extraction is one of the important tasks in face recognition. Moments are widely used feature extractor due to their superior discriminatory power and geometrical invariance. Moments generally capture the global features of the image. This paper proposes Krawtchouk moment for feature extraction in face recognition system, which has the ability to extract local features from any region of interest. Krawtchouk moment is used to extract both local features and global features of the face. The extracted features are fused using summed normalized distance strategy. Nearest neighbour classifier is employed to classify the faces. The proposed method is tested using ORL and Yale databases. Experimental results show that the proposed method is able to recognize images correctly, even if the images are corrupted with noise and possess change in facial expression and tilt.     

A broad review about face recognition – feature extraction and recognition techniques

Abstract

Face recognition (FR) throws open a vast horizon of challenging tasks in the arena of facial image processing applications and computer visualisation, and hence has riveted keen interest during the last few years on account of its versatile applications in numerous spheres. Creating a useful facial design from initial face images is a very important gradient for victorious facial expression detection. Here, we furnish a report of several feature extraction and recognition methods which find themselves employed in the method of FR. The major aim of this survey is to assess the diverse FR methods according to their feature extraction and recognition techniques. From the analysis, we come to know about the feature extraction and recognition methods which have been elegant utilised in the FR procedure. They also vividly establish the technique which has performed excellently yielding superior FR precision by detecting face images more exactly. Moreover our study draws a concise picture of the feature extraction and recognition techniques and acts as a lodestar to the incoming intriguing investigators intending to increase their information about this innovative technique.     

一种多频带线性鉴别分析方法

线性鉴别分析(LDA)是模式识别领域广泛使用的一种特征抽取方法,而在图像识别中,由于小样本问题,经常采用的是PCA LDA方法来代替单纯的LDA.提出了一种多频带线性鉴别分析方法(MBLDA),使LDA在完整的样本空间上进行,而且解决了小样本问题.MBLDA不仅避免了PCA过程带来的信息损失,而且提取的鉴别特征维数小,还提高了识别性能.该方法在识别精度上大幅度地超越了PCA和LDA或PCA LDA,通过对ORL,NUST603人脸库的实验验证了该算法的有效性.     

Probabilistic approach to face recognition

Faces are highly deformable objects which may easily change their appearance over time. Not all face areas are subject to the same variability. Therefore, decoupling of the information from independent areas of the face is of paramount importance to improve the robustness of any face recognition technique. The aim of this article is to present a robust face recognition technique based on the extraction and matching of probabilistic graphs drawn on scale invariant feature transform (SIFT) features related to independent face areas. The face matching strategy is based on matching individual salient facial graphs characterized by SIFT features as connected to facial landmarks such as the eyes and the mouth. In order to reduce the face matching errors, the Dempster–Shafer decision theory is applied to fuse the individual matching scores obtained from each pair of salient facial features. The proposed algorithm is evaluated with the Olivetti Research Lab (ORL) and the Indian Institute of Technology Kanpur (IITK) face databases. The experimental results demonstrate the effectiveness and potential of the proposed face recognition technique, even in the case of partially occluded faces.     

Facial expression recognition by using differential geometric features

ABSTRACT

In recent years, a growing interest has been created for improvement of human interaction with computers. Hence, automatic recognition of facial expressions has become one of the active research topics. The purpose of this paper is to identify facial expressions, by using differential geometric features. In the proposed method, only the first and last images are used and differential features are extracted from these two images. Differential geometric features are extracted from changes in the important points of the face in the two images. In this method, the distance between the important points of the face and the reference point was calculated in both directions x and y, for two images, and with the difference between the distance, the differential geometric features between the two images were obtained. Based on the results, with this method, recognition accuracy of six facial expressions in the database was 96.44%, CK +.     

一种新颖的基于Gabor-LDA的人脸识别方法

为了克服光照、表情变化等因素对人脸识别的影响,提出了一种基于Gabor小波和最佳鉴别分析LDA的人脸识别方法。该方法充分利用了LDA得到的鉴别向量,用鉴别向量组成线性变换矩阵,直接从原始的强度图像上提取LDA特征。然后,用鉴别向量选择一些鉴别像素,仅在鉴别像素的位置上提取Gabor特征并对Gabor特征作LDA变换得到另一种LDA特征。它们分别可视为全局特征和局部特征。最后的分类器融合这两类特征。在FERET人脸库上的试验表明了该方法的有效性。     

Statistical feature extraction based iris recognition system

Iris recognition systems have been proposed by numerous researchers using different feature extraction techniques for accurate and reliable biometric authentication. In this paper, a statistical feature extraction technique based on correlation between adjacent pixels has been proposed and implemented. Hamming distance based metric has been used for matching. Performance of the proposed iris recognition system (IRS) has been measured by recording false acceptance rate (FAR) and false rejection rate (FRR) at different thresholds in the distance metric. System performance has been evaluated by computing statistical features along two directions, namely, radial direction of circular iris region and angular direction extending from pupil to sclera. Experiments have also been conducted to study the effect of number of statistical parameters on FAR and FRR. Results obtained from the experiments based on different set of statistical features of iris images show that there is a significant improvement in equal error rate (EER) when number of statistical parameters for feature extraction is increased from three to six. Further, it has also been found that increasing radial/angular resolution, with normalization in place, improves EER for proposed iris recognition system.     

Face recognition system based on MOTIF features

This work addresses the use of the MOTIF algorithm for face feature extraction. The MOTIF algorithm is commonly used to characterize texture and shows good performance in this task; a MOTIF algorithm without the Co-occurrence Matrix is proposed to obtain face features, and the approach proves to be effective. System testing was based on a standard database (the AR Face database) that includes 120 people, 70 images with face expressions and 30 with sunglasses; 1 to 9 images were used to make the template for each person. After using Euclidean distance, Cosine distance and support vector machine as classifiers, correct classification was achieved with 98% accuracy. Further tests were performed with all databases and compared with Local Binary Pattern, DI-WBP and other commonly used schemes, demonstrating effective face recognition by the MOTIF algorithm without the co-occurrence matrix in addition to its fast performance due to the low computational cost.     

采用图像融合技术的多模式人脸识别

利用图像融合技术实现了基于可见光图像和红外热图像相结合的多模式人脸识别,研究了两种图像在像素级和特征级的融合方法.在像素级,提出了基于小波分解的图像融合方法,实现了两种图像的有效融合.在特征级,采用分别提取两种识别方法中具有较好分类效果的前50%的特征进行特征级的融合.实验表明,经像素级和特征级融合后,识别准确率都较单一图像有很大程度的提高,并且特征级的融合效果明显优于像素级的融合.因此,基于图像融合技术的多模式人脸识别,有效的增加了图像的信息量,是提高人脸识别准确率的有效途径之一.     

A virtual teleconferencing system based on face detection and 3D animation in a low‐bandwidth environment

In this article, we proposed a novel teleconferencing system that combines a facial muscle model and the techniques of face detection and facial feature extraction to synthesize a sequence of life‐like face animation. The proposed system can animate realistic 3D face images in a low‐bandwidth environment to support virtual videoconferencing. Based on the technique of feature extraction, a face detection algorithm for the virtual conferencing system is proposed in this article. In the proposed face detection algorithm, the YCbCr skin color model is used to detect the possible face area of the image; the feature points of the face is determined by using the symmetry property of the face and the gray level characteristics of the eyes and the mouth. According to the positions of the feature points on a facial image, we can compute the transformation values of the feature points. These values will then be sent via a network from the sender's side to the receiver's side frame by frame. We can synthesize the realistic facial animations on the receiver's side based on these. Experimental results show that the proposed system can achieve a practical animated face‐to‐face virtual conference with good facial expressions and a low‐bandwidth requirement. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 323–332, 2010     

Research on Facial Expression Capture Based on Two-Stage Neural Network

To generate realistic three-dimensional animation of virtual character, capturing real facial expression is the primary task. Due to diverse facial expressions and complex background, facial landmarks recognized by existing strategies have the problem of deviations and low accuracy. Therefore, a method for facial expression capture based on two-stage neural network is proposed in this paper which takes advantage of improved multi-task cascaded convolutional networks (MTCNN) and high-resolution network. Firstly, the convolution operation of traditional MTCNN is improved. The face information in the input image is quickly filtered by feature fusion in the first stage and Octave Convolution instead of the original ones is introduced into in the second stage to enhance the feature extraction ability of the network, which further rejects a large number of false candidates. The model outputs more accurate facial candidate windows for better landmarks recognition and locates the faces. Then the images cropped after face detection are input into high-resolution network. Multi-scale feature fusion is realized by parallel connection of multi-resolution streams, and rich high-resolution heatmaps of facial landmarks are obtained. Finally, the changes of facial landmarks recognized are tracked in real-time. The expression parameters are extracted and transmitted to Unity3D engine to drive the virtual character's face, which can realize facial expression synchronous animation. Extensive experimental results obtained on the WFLW database demonstrate the superiority of the proposed method in terms of accuracy and robustness, especially for diverse expressions and complex background. The method can accurately capture facial expression and generate three-dimensional animation effects, making online entertainment and social interaction more immersive in shared virtual space.     

一种基于GDLPP的人脸识别算法

针对人脸识别中的特征提取问题,本文提出了一种结合Gabor小波特征和判别保局投影的人脸识别算法-GDLPP.该算法首先对人脸图像进行多分辨率的Gabor小波变换,提取样本的高阶统计信息;然后更改保局投影(LPP)的目标函数,增加样本类间散布约束,从而提取更具判别性的特征.本文采用最小近邻分类器估算识别率.在USPS数据库、Yale人脸库以及AR人脸库的测试结果表明,在姿态、光照、表情、训练样本数目变化的情况下,GDLPP都具有较好的识别率.     

分块Radon变换在低对比度掌纹特征提取中的应用

特征提取是低对比度掌纹识别的关键步骤.针对掌纹纹理特征明显的特点,本文提出了一种分块Radon变换的掌纹特征提取方法.该方法先对掌纹感兴趣区域进行一级小波分解去噪降维,接着对低频子图像进行分块以圈定局部主要纹理,最后把所有分块后的子图像进行70°～140°Radon变换,所获得的线积分组合在一起构成该图像的特征向量.运...     

Real and Altered Fingerprint Classification Based on Various Features andClassifiers

Biometric recognition refers to the identification of individuals through their unique behavioral features (e.g., fingerprint, face, and iris). We need distinguishing characteristics to identify people, such as fingerprints, which are world-renowned as the most reliable method to identify people. The recognition of fingerprints has become a standard procedure in forensics, and different techniques are available for this purpose. Most current techniques lack interest in image enhancement and rely on high-dimensional features to generate classification models. Therefore, we proposed an effective fingerprint classification method for classifying the fingerprint image as authentic or altered since criminals and hackers routinely change their fingerprints to generate fake ones. In order to improve fingerprint classification accuracy, our proposed method used the most effective texture features and classifiers. Discriminant Analysis (DCA) and Gaussian Discriminant Analysis (GDA) are employed as classifiers, along with Histogram of Oriented Gradient (HOG) and Segmentation-based Feature Texture Analysis (SFTA) feature vectors as inputs. The performance of the classifiers is determined by assessing a range of feature sets, and the most accurate results are obtained. The proposed method is tested using a Sokoto Coventry Fingerprint Dataset (SOCOFing). The SOCOFing project includes 6,000 fingerprint images collected from 600 African people whose fingerprints were taken ten times. Three distinct degrees of obliteration, central rotation, and z-cut have been performed to obtain synthetically altered replicas of the genuine fingerprints. The proposal achieved massive success with a classification accuracy reaching 99%. The experimental results indicate that the proposed method for fingerprint classification is feasible and effective. The experiments also showed that the proposed SFTA-based GDA method outperformed state-of-art approaches in feature dimension and classification accuracy.     

一种新颖的虹膜识别方法

提出一种基于多纹理特征融合的新颖虹膜识别方法。该方法对虹膜图像做Gabor小波变换提取不同分辨力不同方向下的纹理特征作为虹膜的全局特征,在滤波后的子窗口图像上运用灰度级共现矩阵(COM)提取虹膜的局部特征。通过加权欧几里德距离和最小距离分别对全局特征和局部特征进行分类识别。设计了FIS(模糊推理系统)特征融合分类方法来提高虹膜识别的鲁棒性。实验结果表明本方法有效可行,可以达到98.5%的识别率,并在保持1.4%较低的FRR(拒绝率)的同时可以使FAR(误识率)减少到0.1%。     

基于浓度特征向量的目标识别与分类

提出了一种新的基于零件浓度特征向量的目标识别与分类技术,以及由零件图像特征信息所构成的浓度特征信息的合理性验证方法。该技术不仅能够准确地反映目标图像的局部结构特征与整体结构特征之间的关系,而且较好地解决了计算机描述零件图像的特征信息的负担过重问题。实验表明,该技术具有识别准确、计算机负担小的优点。     

A comprehensive study on face recognition: methods and challenges

ABSTRACT

Face Recognition is the process of identifying and verifying the faces. Face recognition has vast importance in the field of Security, Healthcare, Banking, Criminal Identification, Payment, and Advertising. In this paper, we have reviewed various techniques and challenges for the face recognition. Illumination, pose variation, facial expressions, occlusions, aging, etc. are the key challenges to the success of face recognition. Pre-processing, Face Detection, Feature Extraction, Optimal Feature Selection, and Classification are primary steps in any face recognition system. This paper provides a detailed review of each. Feature extraction techniques can be classified as appearance-based methods or geometry-based methods, such method may be local or global. Feature extraction is the most crucial stage for the success of the face recognition system. However, deep learning methods have freed the user from handcrafting the features. In this article, we have surveyed state-of-the-art methods of last few decades and the comparative study of various feature extraction methods is provided. Article also describes the current challenges in the area.     

Occlusion invariant face recognition using mean based weight matrix and support vector machine

In this paper, a novel occlusion invariant face recognition algorithm based on Mean based weight matrix (MBWM) technique is proposed. The proposed algorithm is composed of two phases—the occlusion detection phase and the MBWM based face recognition phase. A feature based approach is used to effectively detect partial occlusions for a given input face image. The input face image is first divided into a finite number of disjointed local patches, and features are extracted for each patch, and the occlusion present is detected. Features obtained from the corresponding occlusion-free patches of training images are used for face image recognition. The SVM classifier is used for occlusion detection for each patch. In the recognition phase, the MBWM bases of occlusion-free image patches are used for face recognition. Euclidean nearest neighbour rule is applied for the matching. GTAV face database that includes many occluded face images by sunglasses and hand are used for the experiment. The experimental results demonstrate that the proposed local patch-based occlusion detection technique works well and the MBWM based method shows superior performance to other conventional approaches.     

一种基于核主成分分析和组合分类器的虹膜识别方法

提出了一种新的虹膜特征提取与识别方法,该方法利用核主成分分析(KPCA)在高维空间具有较强的特征选择能力来提取虹膜图像的纹理特征。采用了一种距离度量和支持向量机相结合的两级分类方法,前级采用欧式距离来度量图像间的相似性,若符合条件,给出分类结果,否则拒绝,并转入后一级分类器——支持向量机分类,以减少进入支持向量机的样本数目,该组合分类方法充分利用了支持向量机识别率高和距离度量速度快的优点。实验结果表明,该方法提高了虹膜识别率,是一种有效的虹膜识别方法。